@inbook {640,
	title = {Multi-Modal Visualization of Probabilistic Tractography},
	booktitle = {Visualization in Medicine and Life Sciences},
	volume = {III},
	year = {2016},
	publisher = {Springer},
	organization = {Springer},
	author = {Mathias Goldau and Mario Hlawitschka}
}
@conference {665,
	title = {Fiber Stipples for Crossing Tracts in Probabilistic Tractography.},
	booktitle = {VCBM},
	year = {2015},
	author = {Andr{\'e} Reichenbach and Mathias Goldau and Mario Hlawitschka}
}
@conference {654,
	title = {Interactive GPU-based Visualization of Scalar Data with Gaussian Distributed Uncertainty},
	booktitle = {Vision, Modeling, and Visualization},
	year = {2015},
	address = {Aachen},
	abstract = {We present a GPU-based approach to visualize samples of normally distributed uncertain, three-dimensional
scalar data. Our approach uses a mathematically sound interpolation scheme, i.e., Gaussian process regression.
The focus of this work is to demonstrate, that GP-regression can be used for interpolation in practice, despite
the high computational costs. The potential of our method is demonstrated by an interactive volume rendering of
three-dimensional data, where the gradient estimation is directly computed by the field function without the need
of additional sample points of the underlying data. We illustrate our method using three-dimensional data sets of
the medical research domain.},
	author = {Steven Schlegel and Mathias Goldau and Gerik Scheuermann}
}
@proceedings {664,
	title = {Visualizing Crossing Probabilistic Tracts},
	year = {2015},
	author = {Mathias Goldau and Andr{\'e} Reichenbach and Mario Hlawitschka}
}
@article {641,
	title = {V{\textendash}Bundles: Clustering Fiber Trajectories from Diffusion MRI in Linear Time},
	journal = {MICCAI},
	volume = {9349},
	year = {2015},
	chapter = {191},
	author = {Andr{\'e} Reichenbach and Mathias Goldau and Christian Heine and Mario Hlawitschka}
}
@conference {642,
	title = {Combined Three-Dimensional Visualization of Structural Connectivity and Cortex Parcellation},
	booktitle = {VMV},
	year = {2014},
	author = {Andr{\'e} Reichenbach and Mathias Goldau and Mario Hlawitschka}
}
@mastersthesis {643,
	title = {Multi-Modal \& Slice-Based Visualizations of Diffusion Tractography Data.},
	year = {2014},
	month = {10/2014},
	school = {Leipzig University},
	type = {Phd Thesis},
	address = {Leipzig, Germany},
	author = {Mathias Goldau}
}
@article {535,
	title = {Cerebellar networks with basal ganglia: feasibility for tracking cerebello-pallidal and subthalamo-cerebellar projections in the human brain},
	journal = {European Journal of Neuroscience},
	year = {2013},
	month = {07/2013},
	type = {Technical Spotlight},
	keywords = {basal ganglia, cerebellum, diffusion magnetic resonance imaging, segmentation protocol, tractography},
	issn = {1460-9568},
	doi = {10.1111/ejn.12314},
	url = {http://dx.doi.org/10.1111/ejn.12314},
	author = {Pelzer, Esther Annegret and Hintzen, Andreas and Mathias Goldau and von Cramon, Detlev Yves and Timmermann, Lars and Tittgemeyer, Marc}
}
@proceedings {531,
	title = {Hierarchical Poisson-Disk Sampling for Fiber Stipples},
	year = {2013},
	month = {09/2013},
	pages = {19-23},
	publisher = {Eurographics},
	address = {Leipzig},
	doi = {10.2312/PE.VMLS.VMLS2013.019-023},
	author = {Mario Hlawitschka and Mathias Goldau and Alexander Wiebel and Heine, C. and Gerik Scheuermann}
}
@conference {435,
	title = {Tractography in Context: Multimodal Visualization of Probabilistic Tractograms in Anatomical Context},
	booktitle = {Visual Computing for Biology and Medicine},
	year = {2012},
	month = {09/2012},
	author = {Anne S. Berres and Mathias Goldau and Tittgemeyer, Marc and Gerik Scheuermann and Hans Hagen}
}
@conference { GWG*:2011,
	title = {Fiber Stippling: An Illustrative Rendering for Probabilistic Diffusion Tractography},
	booktitle = {IEEE BioVis Proceedings},
	year = {2011},
	pages = {23-30},
	abstract = {One of the most promising avenues for compiling anatomical brain connectivity data arises from diffusion magnetic resonance  imaging (dMRI). dMRI provides a rather novel family of medical imaging techniques with broad application in clinical as well as basic neuroscience as it offers an estimate of the brain{\textquoteright}s fiber structure completely non-invasively and in vivo. A convenient way to reconstruct neuronal fiber pathways and to characterize anatomical connectivity from this data is the computation of diffusion tractograms.
In this paper, we present a novel and effective method for visualizing probabilistic tractograms within their anatomical context. Our illustrative rendering technique, called fiber stippling, is inspired by visualization standards as found in anatomical textbooks. These illustrations typically show slice-based projections of fiber pathways and are typically hand-drawn. Applying the automatized technique to diffusion tractography, we demonstrate its expressiveness and intuitive usability as well as a more objective way to present white-matter structure in the human brain.},
	author = {Mathias Goldau and Alexander Wiebel and Nico Stephan Gorbach and Corina Melzer and Mario Hlawitschka and Gerik Scheuermann and Tittgemeyer, Marc}
}
@article {425,
	title = {Hierarchical Information-based Clustering for Connectivity-based Cortex Parcellation},
	journal = {Frontiers in Neuroinformatics},
	volume = {5},
	year = {2011},
	abstract = {One of the most promising avenues for compiling connectivity data originates from the notion that individual brain regions maintain individual connectivity profiles; the functional repertoire of a cortical area (\&quot;the functional fingerprint\&quot;) is closely related to its anatomical connections (\&quot;the connectional fingerprint\&quot;) and, hence, a segregated cortical area may be characterized by a highly coherent connectivity pattern. Diffusion tractography can be used to identify borders between such cortical areas. Each cortical area is defined based upon a unique probabilistic tractogram and such a tractogram is representative of a group of tractograms, thereby forming the cortical area. The underlying methodology is called connectivity-based cortex parcellation, and requires essentially clustering or grouping of similar diffusion tractograms. Despite the relative success of this technique in producing anatomically sensible results, existing clustering techniques in the context of connectivity-based parcellation typically depend on several nontrivial assumptions. In this paper, we embody an unsupervised hierarchical information-based framework to clustering probabilistic tractograms that avoids many drawbacks offered by previous methods. Cortex parcellation of the inferior frontal gyrus together with the precentral gyrus demonstrates a proof of concept of the proposed method: The automatic parcellation reveals cortical subunits consistent with cytoarchitectonic maps and previous studies including connectivity-based parcellation. Further insight into the hierarchically modular architecture of cortical subunits is given by revealing coarser cortical structures that differentiate between primary as well as pre-motoric areas and those associated with pre-frontal areas.},
	issn = {1662-5196},
	doi = {10.3389/fninf.2011.00018},
	url = {http://www.frontiersin.org/Journal/Abstract.aspx?s=752\&name=neuroinformatics\&ART_DOI=10.3389/fninf.2011.00018},
	author = {Nico Stephan Gorbach and Christoph Sch{\"u}tte and Corina Melzer and Mathias Goldau and Olivia Sujazow and Jenia Jitsev and Tania Douglas and Tittgemeyer, Marc}
}
@conference { GWH*:2011,
	title = {Visualizing DTI Parameters on Boundary Surfaces of White Matter Fiber Bundles},
	booktitle = {IASTED International Conference on Computer Graphics and Imaging} publisher = {ACTA Press},
	year = {2011},
	pages = {53-61},
	publisher = {ACTA Press},
	organization = {ACTA Press},
	abstract = {Diffusion magnetic resonance imaging is so far the only medical imaging modality that has the potential for probing anatomical brain connectivity in vivo. Specifically, it provides the data basis for a set of techniques allowing for tracking of fiber bundles in the brain{\textquoteright}s white matter.  Furthermore, due to the micro-structural basis of the diffusion process, fiber integrity might be estimated. Typically, this is achieved by tensor-derived parameters, such as by fractional anisotropy (FA), which allows for a quantification of the directionality of local diffusion properties. In neuroscience, such parameterization of the diffusion tensor has greatly stimulated studies of localized brain changes, related to development, aging, or various neurological and psychiatric diseases.  However, thus far, there is no satisfactory solution for the visualization and assessment of such parameters along fiber bundles.
In this paper, we present a novel technique to visualize changes of tensor-derived parameters along clusters of the trajectories obtained from diffusion tractography. This visualization approach consists of two steps: First, an automatic local aggregation of data values around the trajectories for quantitative analysis and visualization on the fiber bundle boundary and second, a color-coded slice that is intuitively movable along the bundle for interactive exploration of the bundle{\textquoteright}s parameters.},
	isbn = {978-0-88986-865-6},
	author = {Mathias Goldau and Alexander Wiebel and Mario Hlawitschka and Gerik Scheuermann}
}
@booklet {eichelbaum2010d,
	title = {OpenWalnut: A New Tool for Multi-modal Visualization of the Human Brain},
	year = {2010},
	abstract = {In the course of the ongoing research into neurological diseases and the function and anatomy of the brain, a large variety of examination techniques has evolved. The different techniques aim at findings for different research questions or different viewpoints of a single task. Considering the different applications, it is evident that, for many research areas, only a combination of multiple techniques can help answering the posed questions. To name only one example, the combination of dwMRI and fMRI with an anatomical context provided by T1 MRI images is very common. To be able to analyze the data measured by the different techniques, a tool that can efficiently visualize the different modalities simultaneously is needed. The software (called {\em OpenWalnut}) we will present in this poster aims at exactly this task. It does not only allow to display the different modalities together, but also provides tools to analyze their interdependence and relations. In the following, we will provide you with an overview of OpenWalnut{\textquoteright}s features. Its simplicity, making it a useful visualization tool, on the one hand and its powerful and generic framework for computer scientist researchers on the other hand.},
	author = {Eichelbaum, Sebastian and Mathias Goldau and Stefan Philips and Andr{\'e} Reichenbach and Ralph Schurade and Alexander Wiebel}
}
